AIMC Topic: Humans

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Collaborative driving style classification method enabled by majority voting ensemble learning for enhancing classification performance.

PloS one
The classification of driving styles plays a fundamental role in evaluating drivers' driving behaviors, which is of great significance to traffic safety. However, it still suffers from various challenges, including the insufficient accuracy of the mo...

The Use of Robotics in Surgery of Benign Liver Diseases: A Systematic Review.

Surgical innovation
BACKGROUND: Surgical treatment of benign liver diseases (BLD) remains a field of conflict, due to increased risk and high complication rate. However, the introduction of minimally invasive surgery has led to increased number of patients with BLD bein...

Improved biomedical word embeddings in the transformer era.

Journal of biomedical informatics
BACKGROUND: Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g.,...

Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

Journal of biomedical informatics
The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and m...

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

International journal of environmental research and public health
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared ...

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

International journal of environmental research and public health
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) Thi...

The Infectious Disease Ontology in the age of COVID-19.

Journal of biomedical semantics
BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially f...

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Annals of epidemiology
Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal fore...

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.

Genomics, proteomics & bioinformatics
Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor mic...

The PHU-NET: A robust phase unwrapping method for MRI based on deep learning.

Magnetic resonance in medicine
PURPOSE: This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods.